Nocturnal pulse oximetry has been proposed as a tool for diagnosing sleep apnea. We established criteria in determining previous occurrences of apnea events by extracting quantitative characteristics caused by apnea events over the duration of changes in blood oxygen saturation values in our previous studies. In addition, the apnea-hypopnea index was estimated by regression modeling. In this paper, the algorithm presented in the previous study was applied to the data collected from the sleep medicine center of other hospitals to verify its performance. As a result of applying the algorithm to pulse oximetry data of 15 polysomnographic recordings, the minute-by-minute apneic segment detection exhibited an average accuracy of 87.58% and an average Cohen's kappa coefficient of 0.6327. In addition, the correlation coefficient between the estimated apnea-hypopnea index and the reference was 0.95, and the average absolute error was 5.02 events/h. When the algorithm is evaluated on the data collected by the other sleep medicine center, they still detected semi real-time sleep apnea events and showed meaningful results in estimating apnea-hypopnea index, although their performance was somewhat lower than before. With the recent popularity of devices for mobile healthcare, such as the wearable pulse oximeter, the results of this study are expected to improve the user value of devices by implementing mobile sleep apnea diagnosis and monitoring functions.